CN110428011A - A kind of deep learning image fault classification method towards video transmission quality - Google Patents
A kind of deep learning image fault classification method towards video transmission quality Download PDFInfo
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Abstract
The deep learning image fault classification method towards video transmission quality that the invention discloses a kind of selects training loss function, makes training dataset this method comprises: building convolutional neural networks, are arranged training hyper parameter；It concentrates undistorted image to do data enhancing processing data, forms the training sample of more different type of distortion；The method of training sample is randomly selected in design batch training process；Complete neural metwork training, obtain training pattern, after actual deployment can real-time grading video flowing input target image type of distortion.
Description
Technical field
The present invention relates to image fault evaluation of classification field more particularly to a kind of image fault classification based on deep learning
Method.
Background technique
Video image can generate distortion because of various reasons in transmission process, so for occurring in image transmitting process
Distortion correctly classify just and seem critically important.Existing image fault sorting technique much all be rely on human visual system or from
The computation model of right image statistics, even manual sort.It is relatively low that the former is distorted classification accuracy, is easy to appear judgement not
Unanimous circumstances；And then efficiency is very low by the latter, working long hours is easy to appear fatigue error, influences judgment accuracy, seeks thus
A kind of method that can efficiently, accurately, intelligently classify transmission of video images distortion is looked for have important practical significance.
Summary of the invention
In order to solve the above technical problems, the object of the present invention is to provide a kind of deep learning figure towards video transmission quality
Image distortion classification method.
The purpose of the present invention is realized by technical solution below:
A kind of deep learning image fault classification method towards video transmission quality, comprising:
A constructs convolutional neural networks, and training hyper parameter is arranged, and selects training loss function Loss, makes training dataset；
B concentrates undistorted image to do data enhancing processing data, forms the training sample of more different type of distortion；
The method of training sample is randomly selected in C design batch training process；
D completes neural metwork training, obtains training pattern, after actual deployment can the input of real-time grading video flowing target
Image fault type.
Detailed description of the invention
Fig. 1 is the deep learning image fault classification method flow chart towards video transmission quality.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with examples and drawings to this hair
It is bright to be described in further detail.
As shown in Figure 1, a kind of deep learning image fault classification method process towards video transmission quality, including it is following
Step:
Step 10 constructs convolutional neural networks, and training hyper parameter is arranged, and selects training loss function Loss, makes training number
According to collection；
Step 20 concentrates undistorted image to do data enhancing processing data, forms the training sample of more different type of distortion
This；
The method of training sample is randomly selected in step 30 design batch training process；
Step 40 completes neural metwork training, obtains training pattern, can the input of real-time grading video flowing after actual deployment
Target image type of distortion；
Above-mentioned steps 10 specifically include: hyper parameter setting includes maximum train epochs S, single batch number of training N, initial
Learning rate η0, learning rate attenuation rate d, learning rate update interval M, input image size etc..
Above-mentioned steps 10 specifically include: use softmax function as the classifier of CNN, select intersect entropy function as
Loss function.
Above-mentioned steps 10 specifically include: note ηtThe learning rate at interval is updated for the t times study,For the t times study update
Interim least disadvantage functional value, LminFor global minima loss function value, then ηtUpdate method are as follows:
Above-mentioned steps 10 specifically include: altogether including undistorted C in data setc, white noise sound distortion Cn, fuzzy distortion Cb、JPEG
Compression artefacts CJAnd JPEG2000 compression artefacts CJ2000Totally 5 seed type image, and wherein 60% sample is randomly selected from data set
This is as training set, 20% sample as test set, 20% sample as verifying collection.
Above-mentioned steps 20 specifically include: note I is undistorted image, θnWhite noise function parameter, f are added for imagen(I,θn)
White noise function, θ are added for imagebAmbiguity function parameter, f are added for imageb(I,θb) it is that image adds ambiguity function, θJFor
Image JPEG compression function parameter, fJ(I,θJ) it is image JPEG compression function, θJ2000For image JPEG2000 compression function ginseng
Number, fJ2000(I,θJ2000) it is image JPEG2000 compression function, by that can realize in addition to undistorted type other to above-mentioned function
The data of type enhance.
Above-mentioned steps 30 specifically include: note I*Image, C to randomly select are to randomly select image type, fC() is
Data enhancing function, the θ of corresponding typesCFor function fCRandom parameter, random (C) within the scope of () reasonable value are data increasing
Strong random selection function, then I*Are as follows:
Although disclosed herein embodiment it is as above, the content is only to facilitate understanding the present invention and adopting
Embodiment is not intended to limit the invention.Any those skilled in the art to which this invention pertains are not departing from this
Under the premise of the disclosed spirit and scope of invention, any modification and change can be made in the implementing form and in details,
But scope of patent protection of the invention, still should be subject to the scope of the claims as defined in the appended claims.
Claims (7)
1. a kind of deep learning image fault classification method towards video transmission quality, which is characterized in that the described method includes:
A constructs convolutional neural networks, and training hyper parameter is arranged, and selects training loss function Loss, makes training dataset；
B concentrates undistorted image to do data enhancing processing data, forms the training sample of more different type of distortion；
The method of training sample is randomly selected in C design batch training process；
D completes neural metwork training, obtains training pattern, after actual deployment can the input of real-time grading video flowing target image
Type of distortion.
2. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
In the step A, the setting of training hyper parameter includes maximum train epochs S, single batch number of training N, initial learning rate η0, learn
Habit rate attenuation rate d, learning rate update interval M and input image size.
3. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
The step A is specifically included, and uses softmax function as the classifier of CNN, selects to intersect entropy function as loss function.
4. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
In the step A, η is rememberedtThe learning rate at interval is updated for the t times study,The minimum damage between the t times study regeneration interval
Lose functional value, LminFor global minima loss function value, then ηtUpdate method are as follows:
5. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
It altogether include undistorted C in data set in the step Ac, white noise sound distortion Cn, fuzzy distortion Cb, JPEG compression be distorted CJAnd
JPEG2000 compression artefacts CJ2000Totally 5 seed type image, and 60% sample composing training collection, 20% sample are extracted from data set
Constitute test set, 20% sample constitutes verifying collection.
6. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
The step B is specifically included, and note I is undistorted image, θnWhite noise function parameter, f are added for imagen(I,θn) add for image
Add white noise function, θbAmbiguity function parameter, f are added for imageb(I,θb) it is that image adds ambiguity function, θJFor image JPEG
Compression function parameter, fJ(I,θJ) it is image JPEG compression function, θJ2000For image JPEG2000 compression function parameter, fJ2000
(I,θJ2000) it is image JPEG2000 compression function, by realizing the other kinds of data in addition to undistorted type to above-mentioned function
Enhancing.
7. the deep learning image fault classification method towards video transmission quality as described in claim 1, which is characterized in that
The step C is specifically included, and remembers I*Image, C to randomly select are to randomly select image type, fC() is corresponding types
Data enhancing function, θCFor function fCRandom parameter, random (C) within the scope of () reasonable value are data enhancing random selection
Function, then I*Are as follows:
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| CN111815529A (en) * | 2020-06-30 | 2020-10-23 | 上海电力大学 | Low-quality image classification enhancement method based on model fusion and data enhancement |
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